JuiceFS provides standard, flexible and fully managed storage service for the Hadoop ecosystem that enable big data platforms to maximize performance on the cloud.
Tutorial: Using JuiceFS in Big Data Platforms
Best Practice: How to make HBase faster, more stable, and cheaper
Best Practice: Exploring storage and computing separation for ClickHouse
User Case: How to effectively reduce the load of HDFS cluster for Qutoutiao (NASDAQ:QTT)
JuiceFS can manage tens of billions of files, fully POSIX compatible that supports machine learning, deep learning, and parallel computing requirements, and hassle-free use in TensorFlow, PyTorch, MXNet, and other frameworks.
Data backup is crucial for business, but it is not easy to back up a growing cluster. JuiceFS provides fully managed services, elastic scaling with unlimited capacity, and the same experience and compatibility as local disks make backups reliable and straightforward.
Best Practice: MySQL backup, verification and recovery solution based on JuiceFS
JuiceFS is a cloud-native shared file system that replaces traditional NAS and provides data sharing access across thousands of physical machines, virtual machines, and containers.
Real-time data archiving is the basis for mining the value of data. JuiceFS provides data sharing and access to thousands of nodes, easy to deploy, and simple to use.
Best Practice: JuiceFS for archive NGINX logs
User Case: Gaoding Technology Saves 60% Of Storage Cost Used By Elasticsearch
Support multi-region, multi-service provider, the automatic replication automatically backup your important data offsite. When the current region is unavailable, you can quickly switch to the backup service area or service provider to ensure business continuity.
Cloud-native design, support the Cloud Storage Interface standard, seamlessly integrate with your container cluster.
In the process of continuous integration and continuous delivery, JuiceFS provides shared storage for code preparation, construction, product library, and other stages to meet the needs of large-scale build and release.